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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Minimizing data movement 2. Caching and warehouse sizing 3. Query pushdown and execution plans
Snowpark API and Development	30%	- Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies - Python API fundamentals 1. Column operations and functions 2. Data persistence and writing results 3. DataFrame creation from tables, views, SQL
Snowpark Concepts and Architecture	25%	- Session management and connection 1. Create and configure Snowpark sessions 2. Authentication and connection settings - Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Lazy evaluation and DAG execution 3. Transformations vs actions
Data Transformations and Operations	35%	- DataFrame manipulation 1. Joins, unions, set operations 2. Selection, projection, renaming, casting 3. Filtering, sorting, grouping, aggregation - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs - Advanced operations 1. Pivot and unpivot transformations 2. Semi-structured data processing 3. Window functions and analytics

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q320-Q325):

質問 # 320
You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?

A. Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.
B. Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.
C. Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
D. Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
E. Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.

正解：A

解説：
Option C is the most efficient and recommended approach. Snowflake allows importing dependencies from a stage as a ZIP file. This avoids the complexity of creating a custom Anaconda package (Option B) or manually managing dependencies on each virtual warehouse node (Option D), which is not supported. Directly including the code (Option E) makes the procedure large and difficult to manage. Using (Option A) is generally discouraged as it's less robust for dependency management in Snowpark stored procedures.

質問 # 321
Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?

正解：D

解説：
Option A correctly groups the 'employees' DataFrame by 'department_id' , calculates the maximum salary for each department using and then joins the resulting DataFrame with the 'departments' DataFrame on Finally, it selects the required columns: department name, location, and the calculated maximum salary. Option B is not correct as it calculates the rank of all salaries within each department but it does not aggregate on 'department_id'. Option C incorrectly attempts to group by columns from both tables before joining. Option D incorrectly uses the un-joined employees table where rank_salaries are from department_id. Option E will not work due to table names during select. It tries to use 'joined_df table for columns which doesn't have it.

質問 # 322
You are developing a Snowpark application that uses a UDF written in Python. This UDF requires several third-party Python packages (e.g., 'pandas', 'scikit-learn'). You need to ensure these packages are available in the UDF's execution environment within Snowflake. You have the packages listed in a 'requirements.txt' file. Which of the following methods, alone or in combination, correctly provision these dependencies for the UDF? (Select all that apply)

A. Include the packages directly in the '@udf decorator's 'packages' argument as strings (e.g., 'scikit-learnT).
B. Create a Conda environment with the required packages and upload the resulting environment file to a Snowflake stage. Specify the stage location in the 'conda_integration' argument of the 'session.udf.register' method or guff decorator.
C. Use the 'session.add_packages' method to add the packages programmatically before registering the UDF.
D. Manually install the packages on the Snowflake compute warehouse nodes using SSH.
E. Upload the 'requirements.txt file to a Snowflake stage and specify the stage location in the 'imports' argument of the '@udf decorator.

正解：A、B、C

解説：
Options A, C and E are correct. Option A: Directly including packages as strings in the '@udf decorator's "packages' argument is a valid way to specify dependencies when using the Snowflake Anaconda channel. Option C: Using a Conda environment file is the most robust way to manage dependencies, especially for complex projects with specific version requirements. Uploading the environment and referencing it through conda_integratiorü ensures consistent dependency resolution. Option E: Using the 'session.add_packageS method is a viable way to add packages dynamically before UDF registration. Option B is incorrect: you cannot directly import a 'requirements.txt' file using 'imports'. Option D is incorrect: You cannot manually install packages on the Snowflake compute warehouse.

質問 # 323
Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.
B. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.
C. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.
D. 1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.
E. 1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.

正解：B

解説：
Option A correctly outlines the process. First, the stored procedure is created with the correct syntax, including specifying the runtime version and handler, and using 'EXECUTE AS OWNER. Second, 'USAGE privilege is granted on the database and schema. Third, EXECUTE PROCEDURE' privilege must be explicitly granted to the 'ANALYST role. Option B attempts to embed the python code inline but doesn't include IMPORTS section if needed and its more common now to upload the Python code using create or replace procedure syntax. Option C is incorrect because you need to grant ' EXECUTE PROCEDURE privilege to the role in addition to Usage. Option D uses 'EXECUTE AS CALLER , which is not what the question specifies and requires granting 'SELECT privileges on the underlying tables, defeating the purpose of owner rights. 'EXECUTE' is not a valid privilege to grant on stored procedures in Snowflake (Option E), it has to be 'EXECUTE PROCEDURE.

質問 # 324
You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

A. Use 'df.checkpoint()' to truncate the DataFrame lineage. This will prevent re-computation in any downstream operations. Monitor the impact on storage costs.
B. Using a local variable to store the DataFrame. This method is most suitable for materializing the results of the DataFrame.
C. Use to materialize the DataFrame in memory. This is the most efficient approach as it minimizes disk I/O. Consider the size of the DataFrame relative to available memory to avoid memory pressure.
D. Create a temporary table using 'df.write.save_as_table('temp_transactions', temporary-True)'. This persists the DataFrame to Snowflake storage, reducing the need for repeated computations. Monitor the size of the temporary table and its impact on storage costs.
E. Write the DataFrame to a persistent Snowflake table using and then read it back into a new DataFrame. This ensures data persistence but may introduce overhead due to data serialization and deserialization. Only use this method if persistence is required beyond the session.

正解：C、D

解説：
Using materializes the DataFrame in memory, which is faster for repeated access but requires sufficient memory. Creating a temporary table using temporary=TrueV persists the DataFrame to Snowflake storage, reducing recomputation at the cost of storage 1/0. Choosing between these options depends on the DataFrame's size, available memory, and the frequency of access. Writing to a persistent table adds unnecessary overhead unless persistence is required. Using a local variable will only persist the result within the scope of that variable, not across multiple Snowpark operations. Checkpointing is used for lineage truncation not caching.

質問 # 325
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